Overview

Dataset statistics

Number of variables18
Number of observations181691
Missing cells234487
Missing cells (%)7.2%
Duplicate rows225
Duplicate rows (%)0.1%
Total size in memory174.2 MiB
Average record size in memory1005.5 B

Variable types

Numeric7
Categorical11

Warnings

Dataset has 225 (0.1%) duplicate rowsDuplicates
Country has a high cardinality: 205 distinct values High cardinality
state has a high cardinality: 2854 distinct values High cardinality
city has a high cardinality: 36674 distinct values High cardinality
Target has a high cardinality: 86006 distinct values High cardinality
Summary has a high cardinality: 112492 distinct values High cardinality
Group has a high cardinality: 3537 distinct values High cardinality
Motive has a high cardinality: 14490 distinct values High cardinality
Killed is highly correlated with WoundedHigh correlation
Wounded is highly correlated with KilledHigh correlation
latitude is highly correlated with Region and 1 other fieldsHigh correlation
AttackType is highly correlated with Weapon_typeHigh correlation
Region is highly correlated with latitude and 1 other fieldsHigh correlation
Wounded is highly correlated with KilledHigh correlation
Weapon_type is highly correlated with AttackTypeHigh correlation
Year is highly correlated with latitude and 1 other fieldsHigh correlation
Killed is highly correlated with WoundedHigh correlation
Weapon_type is highly correlated with AttackTypeHigh correlation
AttackType is highly correlated with Weapon_typeHigh correlation
latitude has 4556 (2.5%) missing values Missing
longitude has 4557 (2.5%) missing values Missing
Killed has 10313 (5.7%) missing values Missing
Wounded has 16311 (9.0%) missing values Missing
Summary has 66129 (36.4%) missing values Missing
Motive has 131130 (72.2%) missing values Missing
longitude is highly skewed (γ1 = -420.8728522) Skewed
Killed is highly skewed (γ1 = 54.22986883) Skewed
Wounded is highly skewed (γ1 = 174.7020782) Skewed
Summary is uniformly distributed Uniform
Killed has 88149 (48.5%) zeros Zeros
Wounded has 103275 (56.8%) zeros Zeros

Reproduction

Analysis started2021-08-08 03:29:05.726380
Analysis finished2021-08-08 03:31:17.696398
Duration2 minutes and 11.97 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

Year
Real number (ℝ≥0)

HIGH CORRELATION

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2002.638997
Minimum1970
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-08-08T09:01:18.292940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1970
5-th percentile1979
Q11991
median2009
Q32014
95-th percentile2017
Maximum2017
Range47
Interquartile range (IQR)23

Descriptive statistics

Standard deviation13.25943047
Coefficient of variation (CV)0.006620978862
Kurtosis-1.043063969
Mean2002.638997
Median Absolute Deviation (MAD)7
Skewness-0.6190673454
Sum363861482
Variance175.8124963
MonotonicityNot monotonic
2021-08-08T09:01:18.574165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
201416903
 
9.3%
201514965
 
8.2%
201613587
 
7.5%
201312036
 
6.6%
201710900
 
6.0%
20128522
 
4.7%
20115076
 
2.8%
19925071
 
2.8%
20104826
 
2.7%
20084805
 
2.6%
Other values (37)85000
46.8%
ValueCountFrequency (%)
1970651
 
0.4%
1971471
 
0.3%
1972568
 
0.3%
1973473
 
0.3%
1974581
 
0.3%
1975740
 
0.4%
1976923
 
0.5%
19771319
0.7%
19781526
0.8%
19792662
1.5%
ValueCountFrequency (%)
201710900
6.0%
201613587
7.5%
201514965
8.2%
201416903
9.3%
201312036
6.6%
20128522
4.7%
20115076
 
2.8%
20104826
 
2.7%
20094721
 
2.6%
20084805
 
2.6%

Month
Real number (ℝ≥0)

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.46727686
Minimum0
Maximum12
Zeros20
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-08-08T09:01:18.761617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6
Q39
95-th percentile12
Maximum12
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.388303394
Coefficient of variation (CV)0.5239150059
Kurtosis-1.171516408
Mean6.46727686
Median Absolute Deviation (MAD)3
Skewness0.006750446764
Sum1175046
Variance11.48059989
MonotonicityNot monotonic
2021-08-08T09:01:18.964659image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
516875
9.3%
716268
9.0%
815800
8.7%
1015563
8.6%
615359
8.5%
315257
8.4%
415152
8.3%
114936
8.2%
1114906
8.2%
914180
7.8%
Other values (3)27395
15.1%
ValueCountFrequency (%)
020
 
< 0.1%
114936
8.2%
213879
7.6%
315257
8.4%
415152
8.3%
516875
9.3%
615359
8.5%
716268
9.0%
815800
8.7%
914180
7.8%
ValueCountFrequency (%)
1213496
7.4%
1114906
8.2%
1015563
8.6%
914180
7.8%
815800
8.7%
716268
9.0%
615359
8.5%
516875
9.3%
415152
8.3%
315257
8.4%

Day
Real number (ℝ≥0)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5056442
Minimum0
Maximum31
Zeros891
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-08-08T09:01:19.183362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median15
Q323
95-th percentile29
Maximum31
Range31
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.814044752
Coefficient of variation (CV)0.5684410554
Kurtosis-1.181855089
Mean15.5056442
Median Absolute Deviation (MAD)8
Skewness0.01906533556
Sum2817236
Variance77.6873849
MonotonicityNot monotonic
2021-08-08T09:01:19.370816image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
156500
 
3.6%
16344
 
3.5%
46153
 
3.4%
166112
 
3.4%
106064
 
3.3%
96057
 
3.3%
136043
 
3.3%
146028
 
3.3%
286027
 
3.3%
126012
 
3.3%
Other values (22)120351
66.2%
ValueCountFrequency (%)
0891
 
0.5%
16344
3.5%
25954
3.3%
36011
3.3%
46153
3.4%
55844
3.2%
65781
3.2%
75997
3.3%
85859
3.2%
96057
3.3%
ValueCountFrequency (%)
313095
1.7%
305046
2.8%
295507
3.0%
286027
3.3%
275937
3.3%
265823
3.2%
255875
3.2%
245752
3.2%
235782
3.2%
225799
3.2%

Country
Categorical

HIGH CARDINALITY

Distinct205
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.2 MiB
Iraq
24636 
Pakistan
14368 
Afghanistan
12731 
India
11960 
Colombia
 
8306
Other values (200)
109690 

Length

Max length32
Median length7
Mean length7.655403955
Min length4

Characters and Unicode

Total characters1390918
Distinct characters56
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)< 0.1%

Sample

1st rowDominican Republic
2nd rowMexico
3rd rowPhilippines
4th rowGreece
5th rowJapan

Common Values

ValueCountFrequency (%)
Iraq24636
 
13.6%
Pakistan14368
 
7.9%
Afghanistan12731
 
7.0%
India11960
 
6.6%
Colombia8306
 
4.6%
Philippines6908
 
3.8%
Peru6096
 
3.4%
El Salvador5320
 
2.9%
United Kingdom5235
 
2.9%
Turkey4292
 
2.4%
Other values (195)81839
45.0%

Length

2021-08-08T09:01:19.933180image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
iraq24636
 
11.4%
pakistan14368
 
6.7%
afghanistan12731
 
5.9%
india11960
 
5.5%
colombia8306
 
3.9%
united8093
 
3.8%
philippines6908
 
3.2%
peru6096
 
2.8%
el5320
 
2.5%
salvador5320
 
2.5%
Other values (222)111782
51.9%

Most occurring characters

ValueCountFrequency (%)
a213405
15.3%
i136887
 
9.8%
n116940
 
8.4%
e77088
 
5.5%
r76385
 
5.5%
t58869
 
4.2%
l52927
 
3.8%
s51007
 
3.7%
o46690
 
3.4%
d44148
 
3.2%
Other values (46)516572
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1142771
82.2%
Uppercase Letter212974
 
15.3%
Space Separator33829
 
2.4%
Open Punctuation579
 
< 0.1%
Close Punctuation579
 
< 0.1%
Dash Punctuation179
 
< 0.1%
Other Punctuation7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a213405
18.7%
i136887
12.0%
n116940
10.2%
e77088
 
6.7%
r76385
 
6.7%
t58869
 
5.2%
l52927
 
4.6%
s51007
 
4.5%
o46690
 
4.1%
d44148
 
3.9%
Other values (16)268425
23.5%
Uppercase Letter
ValueCountFrequency (%)
I42180
19.8%
P27888
13.1%
S27532
12.9%
A19864
9.3%
C13238
 
6.2%
U10377
 
4.9%
T8651
 
4.1%
E8307
 
3.9%
L7982
 
3.7%
G7911
 
3.7%
Other values (14)39044
18.3%
Other Punctuation
ValueCountFrequency (%)
'4
57.1%
.3
42.9%
Space Separator
ValueCountFrequency (%)
33829
100.0%
Open Punctuation
ValueCountFrequency (%)
(579
100.0%
Close Punctuation
ValueCountFrequency (%)
)579
100.0%
Dash Punctuation
ValueCountFrequency (%)
-179
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1355745
97.5%
Common35173
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a213405
15.7%
i136887
 
10.1%
n116940
 
8.6%
e77088
 
5.7%
r76385
 
5.6%
t58869
 
4.3%
l52927
 
3.9%
s51007
 
3.8%
o46690
 
3.4%
d44148
 
3.3%
Other values (40)481399
35.5%
Common
ValueCountFrequency (%)
33829
96.2%
(579
 
1.6%
)579
 
1.6%
-179
 
0.5%
'4
 
< 0.1%
.3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1390918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a213405
15.3%
i136887
 
9.8%
n116940
 
8.4%
e77088
 
5.5%
r76385
 
5.5%
t58869
 
4.2%
l52927
 
3.8%
s51007
 
3.7%
o46690
 
3.4%
d44148
 
3.2%
Other values (46)516572
37.1%

state
Categorical

HIGH CARDINALITY

Distinct2854
Distinct (%)1.6%
Missing421
Missing (%)0.2%
Memory size11.5 MiB
Baghdad
 
7645
Northern Ireland
 
4498
Unknown
 
4290
Balochistan
 
3710
Saladin
 
3411
Other values (2849)
157716 

Length

Max length47
Median length7
Mean length9.257687428
Min length2

Characters and Unicode

Total characters1678141
Distinct characters69
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique916 ?
Unique (%)0.5%

Sample

1st rowFederal
2nd rowTarlac
3rd rowAttica
4th rowFukouka
5th rowIllinois

Common Values

ValueCountFrequency (%)
Baghdad7645
 
4.2%
Northern Ireland4498
 
2.5%
Unknown4290
 
2.4%
Balochistan3710
 
2.0%
Saladin3411
 
1.9%
Al Anbar3299
 
1.8%
Nineveh3241
 
1.8%
Sindh3206
 
1.8%
Khyber Pakhtunkhwa3084
 
1.7%
Diyala3041
 
1.7%
Other values (2844)141845
78.1%

Length

2021-08-08T09:01:20.511208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
baghdad7645
 
3.1%
northern5772
 
2.4%
ireland4499
 
1.8%
al4352
 
1.8%
unknown4291
 
1.8%
north3775
 
1.5%
balochistan3741
 
1.5%
saladin3411
 
1.4%
anbar3299
 
1.4%
nineveh3242
 
1.3%
Other values (2550)199705
81.9%

Most occurring characters

ValueCountFrequency (%)
a247826
 
14.8%
n130006
 
7.7%
r110710
 
6.6%
i103472
 
6.2%
e99692
 
5.9%
t81482
 
4.9%
o78649
 
4.7%
h72361
 
4.3%
l68100
 
4.1%
62470
 
3.7%
Other values (59)623373
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1362322
81.2%
Uppercase Letter242600
 
14.5%
Space Separator62470
 
3.7%
Dash Punctuation4551
 
0.3%
Open Punctuation2612
 
0.2%
Close Punctuation2612
 
0.2%
Other Symbol485
 
< 0.1%
Decimal Number247
 
< 0.1%
Other Punctuation242
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B31020
12.8%
A27191
11.2%
S26253
10.8%
N20783
 
8.6%
K14977
 
6.2%
C14945
 
6.2%
P13561
 
5.6%
M12213
 
5.0%
D9321
 
3.8%
L8493
 
3.5%
Other values (16)63843
26.3%
Lowercase Letter
ValueCountFrequency (%)
a247826
18.2%
n130006
9.5%
r110710
 
8.1%
i103472
 
7.6%
e99692
 
7.3%
t81482
 
6.0%
o78649
 
5.8%
h72361
 
5.3%
l68100
 
5.0%
d61976
 
4.5%
Other values (16)308048
22.6%
Decimal Number
ValueCountFrequency (%)
364
25.9%
146
18.6%
236
14.6%
533
13.4%
429
11.7%
724
 
9.7%
615
 
6.1%
Other Punctuation
ValueCountFrequency (%)
'214
88.4%
.13
 
5.4%
,6
 
2.5%
&5
 
2.1%
/4
 
1.7%
Space Separator
ValueCountFrequency (%)
62470
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4551
100.0%
Other Symbol
ValueCountFrequency (%)
485
100.0%
Open Punctuation
ValueCountFrequency (%)
(2612
100.0%
Close Punctuation
ValueCountFrequency (%)
)2612
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1604922
95.6%
Common73219
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a247826
15.4%
n130006
 
8.1%
r110710
 
6.9%
i103472
 
6.4%
e99692
 
6.2%
t81482
 
5.1%
o78649
 
4.9%
h72361
 
4.5%
l68100
 
4.2%
d61976
 
3.9%
Other values (42)550648
34.3%
Common
ValueCountFrequency (%)
62470
85.3%
-4551
 
6.2%
(2612
 
3.6%
)2612
 
3.6%
485
 
0.7%
'214
 
0.3%
364
 
0.1%
146
 
0.1%
236
 
< 0.1%
533
 
< 0.1%
Other values (7)96
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1677656
> 99.9%
Specials485
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a247826
 
14.8%
n130006
 
7.7%
r110710
 
6.6%
i103472
 
6.2%
e99692
 
5.9%
t81482
 
4.9%
o78649
 
4.7%
h72361
 
4.3%
l68100
 
4.1%
62470
 
3.7%
Other values (58)622888
37.1%
Specials
ValueCountFrequency (%)
485
100.0%

Region
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.9 MiB
Middle East & North Africa
50474 
South Asia
44974 
South America
18978 
Sub-Saharan Africa
17550 
Western Europe
16639 
Other values (7)
33076 

Length

Max length27
Median length14
Mean length17.32910271
Min length9

Characters and Unicode

Total characters3148542
Distinct characters28
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCentral America & Caribbean
2nd rowNorth America
3rd rowSoutheast Asia
4th rowWestern Europe
5th rowEast Asia

Common Values

ValueCountFrequency (%)
Middle East & North Africa50474
27.8%
South Asia44974
24.8%
South America18978
 
10.4%
Sub-Saharan Africa17550
 
9.7%
Western Europe16639
 
9.2%
Southeast Asia12485
 
6.9%
Central America & Caribbean10344
 
5.7%
Eastern Europe5144
 
2.8%
North America3456
 
1.9%
East Asia802
 
0.4%
Other values (2)845
 
0.5%

Length

2021-08-08T09:01:21.151646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
africa68024
12.7%
south63952
11.9%
61100
11.4%
asia58824
11.0%
north53930
10.1%
east51276
9.6%
middle50474
9.4%
america32778
6.1%
europe21783
 
4.1%
sub-saharan17550
 
3.3%
Other values (7)56083
10.5%

Most occurring characters

ValueCountFrequency (%)
354083
 
11.2%
a314186
 
10.0%
r237381
 
7.5%
t227100
 
7.2%
i221008
 
7.0%
e177475
 
5.6%
A159908
 
5.1%
o152150
 
4.8%
h147917
 
4.7%
s144932
 
4.6%
Other values (18)1012402
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2223585
70.6%
Uppercase Letter492224
 
15.6%
Space Separator354083
 
11.2%
Other Punctuation61100
 
1.9%
Dash Punctuation17550
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a314186
14.1%
r237381
10.7%
t227100
10.2%
i221008
9.9%
e177475
8.0%
o152150
 
6.8%
h147917
 
6.7%
s144932
 
6.5%
u116052
 
5.2%
c101084
 
4.5%
Other values (7)384300
17.3%
Uppercase Letter
ValueCountFrequency (%)
A159908
32.5%
S111537
22.7%
E78203
15.9%
N53930
 
11.0%
M50474
 
10.3%
C21251
 
4.3%
W16639
 
3.4%
O282
 
0.1%
Space Separator
ValueCountFrequency (%)
354083
100.0%
Other Punctuation
ValueCountFrequency (%)
&61100
100.0%
Dash Punctuation
ValueCountFrequency (%)
-17550
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2715809
86.3%
Common432733
 
13.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a314186
 
11.6%
r237381
 
8.7%
t227100
 
8.4%
i221008
 
8.1%
e177475
 
6.5%
A159908
 
5.9%
o152150
 
5.6%
h147917
 
5.4%
s144932
 
5.3%
u116052
 
4.3%
Other values (15)817700
30.1%
Common
ValueCountFrequency (%)
354083
81.8%
&61100
 
14.1%
-17550
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3148542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
354083
 
11.2%
a314186
 
10.0%
r237381
 
7.5%
t227100
 
7.2%
i221008
 
7.0%
e177475
 
5.6%
A159908
 
5.1%
o152150
 
4.8%
h147917
 
4.7%
s144932
 
4.6%
Other values (18)1012402
32.2%

city
Categorical

HIGH CARDINALITY

Distinct36674
Distinct (%)20.2%
Missing434
Missing (%)0.2%
Memory size11.3 MiB
Unknown
 
9775
Baghdad
 
7589
Karachi
 
2652
Lima
 
2359
Mosul
 
2265
Other values (36669)
156617 

Length

Max length65
Median length7
Mean length8.516923484
Min length2

Characters and Unicode

Total characters1543752
Distinct characters72
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24525 ?
Unique (%)13.5%

Sample

1st rowSanto Domingo
2nd rowMexico city
3rd rowUnknown
4th rowAthens
5th rowFukouka

Common Values

ValueCountFrequency (%)
Unknown9775
 
5.4%
Baghdad7589
 
4.2%
Karachi2652
 
1.5%
Lima2359
 
1.3%
Mosul2265
 
1.2%
Belfast2171
 
1.2%
Santiago1621
 
0.9%
Mogadishu1581
 
0.9%
San Salvador1558
 
0.9%
Istanbul1048
 
0.6%
Other values (36664)148638
81.8%

Length

2021-08-08T09:01:21.838982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
district13625
 
5.9%
unknown9822
 
4.3%
baghdad7590
 
3.3%
san3810
 
1.7%
karachi2660
 
1.2%
lima2382
 
1.0%
mosul2284
 
1.0%
city2230
 
1.0%
belfast2181
 
0.9%
santiago1716
 
0.7%
Other values (34014)181598
79.0%

Most occurring characters

ValueCountFrequency (%)
a240843
 
15.6%
i116278
 
7.5%
n101715
 
6.6%
r87518
 
5.7%
o76361
 
4.9%
t71004
 
4.6%
e62716
 
4.1%
d61414
 
4.0%
h59400
 
3.8%
l58736
 
3.8%
Other values (62)607767
39.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1273364
82.5%
Uppercase Letter216832
 
14.0%
Space Separator48659
 
3.2%
Dash Punctuation3711
 
0.2%
Open Punctuation362
 
< 0.1%
Close Punctuation362
 
< 0.1%
Other Punctuation337
 
< 0.1%
Other Symbol69
 
< 0.1%
Decimal Number54
 
< 0.1%
Math Symbol2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B28895
13.3%
S23044
 
10.6%
M19480
 
9.0%
K17108
 
7.9%
A15983
 
7.4%
C11462
 
5.3%
U11015
 
5.1%
T10074
 
4.6%
P9393
 
4.3%
D9242
 
4.3%
Other values (16)61136
28.2%
Lowercase Letter
ValueCountFrequency (%)
a240843
18.9%
i116278
 
9.1%
n101715
 
8.0%
r87518
 
6.9%
o76361
 
6.0%
t71004
 
5.6%
e62716
 
4.9%
d61414
 
4.8%
h59400
 
4.7%
l58736
 
4.6%
Other values (16)337379
26.5%
Other Punctuation
ValueCountFrequency (%)
'170
50.4%
.96
28.5%
,29
 
8.6%
*20
 
5.9%
/20
 
5.9%
&1
 
0.3%
?1
 
0.3%
Decimal Number
ValueCountFrequency (%)
019
35.2%
515
27.8%
210
18.5%
63
 
5.6%
43
 
5.6%
12
 
3.7%
32
 
3.7%
Space Separator
ValueCountFrequency (%)
48659
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3711
100.0%
Other Symbol
ValueCountFrequency (%)
69
100.0%
Open Punctuation
ValueCountFrequency (%)
(362
100.0%
Close Punctuation
ValueCountFrequency (%)
)362
100.0%
Math Symbol
ValueCountFrequency (%)
=2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1490196
96.5%
Common53556
 
3.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a240843
16.2%
i116278
 
7.8%
n101715
 
6.8%
r87518
 
5.9%
o76361
 
5.1%
t71004
 
4.8%
e62716
 
4.2%
d61414
 
4.1%
h59400
 
4.0%
l58736
 
3.9%
Other values (42)554211
37.2%
Common
ValueCountFrequency (%)
48659
90.9%
-3711
 
6.9%
(362
 
0.7%
)362
 
0.7%
'170
 
0.3%
.96
 
0.2%
69
 
0.1%
,29
 
0.1%
*20
 
< 0.1%
/20
 
< 0.1%
Other values (10)58
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII1543683
> 99.9%
Specials69
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a240843
 
15.6%
i116278
 
7.5%
n101715
 
6.6%
r87518
 
5.7%
o76361
 
4.9%
t71004
 
4.6%
e62716
 
4.1%
d61414
 
4.0%
h59400
 
3.8%
l58736
 
3.8%
Other values (61)607698
39.4%
Specials
ValueCountFrequency (%)
69
100.0%

latitude
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct48322
Distinct (%)27.3%
Missing4556
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean23.49834296
Minimum-53.154613
Maximum74.633553
Zeros0
Zeros (%)0.0%
Negative15782
Negative (%)8.7%
Memory size1.4 MiB
2021-08-08T09:01:22.137309image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-53.154613
5-th percentile-12.068306
Q111.510046
median31.467463
Q334.685087
95-th percentile48.694492
Maximum74.633553
Range127.788166
Interquartile range (IQR)23.175041

Descriptive statistics

Standard deviation18.56924242
Coefficient of variation (CV)0.79023625
Kurtosis0.8606639838
Mean23.49834296
Median Absolute Deviation (MAD)9.1532
Skewness-0.9637382662
Sum4162378.98
Variance344.8167641
MonotonicityNot monotonic
2021-08-08T09:01:22.420943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.3035667521
 
4.1%
24.8911152686
 
1.5%
-11.9673682401
 
1.3%
36.3541452247
 
1.2%
54.6077122189
 
1.2%
-33.3662381562
 
0.9%
13.692881559
 
0.9%
2.0598191536
 
0.8%
41.1061781052
 
0.6%
37.997491017
 
0.6%
Other values (48312)153365
84.4%
(Missing)4556
 
2.5%
ValueCountFrequency (%)
-53.1546135
< 0.1%
-51.6922141
 
< 0.1%
-45.8678895
< 0.1%
-45.5711122
 
< 0.1%
-45.4035441
 
< 0.1%
-43.5320544
< 0.1%
-42.8840491
 
< 0.1%
-42.2504581
 
< 0.1%
-41.4729032
 
< 0.1%
-41.2865451
 
< 0.1%
ValueCountFrequency (%)
74.6335531
< 0.1%
65.8251191
< 0.1%
65.6833681
< 0.1%
65.0120891
< 0.1%
64.8377781
< 0.1%
64.6244711
< 0.1%
64.551
< 0.1%
64.3983071
< 0.1%
64.1353382
< 0.1%
64.0750551
< 0.1%

longitude
Real number (ℝ)

MISSING
SKEWED

Distinct48039
Distinct (%)27.1%
Missing4557
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean-458.695653
Minimum-86185896
Maximum179.366667
Zeros0
Zeros (%)0.0%
Negative40531
Negative (%)22.3%
Memory size1.4 MiB
2021-08-08T09:01:22.712166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-86185896
5-th percentile-87.2167
Q14.54563975
median43.246506
Q368.710327
95-th percentile101.797961
Maximum179.366667
Range86186075.37
Interquartile range (IQR)64.16468725

Descriptive statistics

Standard deviation204778.9886
Coefficient of variation (CV)-446.4376047
Kurtosis177133.9718
Mean-458.695653
Median Absolute Deviation (MAD)28.290924
Skewness-420.8728522
Sum-81250595.8
Variance4.193443418 × 1010
MonotonicityNot monotonic
2021-08-08T09:01:22.990735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44.3717737521
 
4.1%
67.1433112686
 
1.5%
-76.9784622401
 
1.3%
43.143572247
 
1.2%
-5.956212189
 
1.2%
-70.5053021562
 
0.9%
-89.1991611559
 
0.9%
45.3261151536
 
0.8%
28.6898631052
 
0.6%
23.7627281017
 
0.6%
Other values (48029)153364
84.4%
(Missing)4557
 
2.5%
ValueCountFrequency (%)
-861858961
 
< 0.1%
-176.1764471
 
< 0.1%
-157.8583331
 
< 0.1%
-157.8189682
< 0.1%
-157.7394441
 
< 0.1%
-149.5695043
< 0.1%
-147.7163891
 
< 0.1%
-124.2249961
 
< 0.1%
-124.217891
 
< 0.1%
-124.1636732
< 0.1%
ValueCountFrequency (%)
179.3666671
 
< 0.1%
179.338111
 
< 0.1%
178.44198
< 0.1%
178.4389791
 
< 0.1%
177.8498332
 
< 0.1%
177.4527784
 
< 0.1%
176.9867561
 
< 0.1%
175.0707831
 
< 0.1%
174.7762821
 
< 0.1%
174.77623610
< 0.1%

AttackType
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.8 MiB
Bombing/Explosion
88255 
Armed Assault
42669 
Assassination
19312 
Hostage Taking (Kidnapping)
11158 
Facility/Infrastructure Attack
10356 
Other values (4)
9941 

Length

Max length35
Median length17
Mean length16.64808383
Min length7

Characters and Unicode

Total characters3024807
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAssassination
2nd rowHostage Taking (Kidnapping)
3rd rowAssassination
4th rowBombing/Explosion
5th rowFacility/Infrastructure Attack

Common Values

ValueCountFrequency (%)
Bombing/Explosion88255
48.6%
Armed Assault42669
23.5%
Assassination19312
 
10.6%
Hostage Taking (Kidnapping)11158
 
6.1%
Facility/Infrastructure Attack10356
 
5.7%
Unknown7276
 
4.0%
Unarmed Assault1015
 
0.6%
Hostage Taking (Barricade Incident)991
 
0.5%
Hijacking659
 
0.4%

Length

2021-08-08T09:01:23.459375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-08T09:01:23.663689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
bombing/explosion88255
33.8%
assault43684
16.7%
armed42669
16.3%
assassination19312
 
7.4%
hostage12149
 
4.7%
taking12149
 
4.7%
kidnapping11158
 
4.3%
facility/infrastructure10356
 
4.0%
attack10356
 
4.0%
unknown7276
 
2.8%
Other values (4)3656
 
1.4%

Most occurring characters

ValueCountFrequency (%)
o303502
 
10.0%
n285439
 
9.4%
s275376
 
9.1%
i273611
 
9.0%
a152488
 
5.0%
l142295
 
4.7%
m131939
 
4.4%
t127916
 
4.2%
g124370
 
4.1%
A116021
 
3.8%
Other values (25)1091850
36.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2462938
81.4%
Uppercase Letter359631
 
11.9%
Other Punctuation98611
 
3.3%
Space Separator79329
 
2.6%
Open Punctuation12149
 
0.4%
Close Punctuation12149
 
0.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o303502
12.3%
n285439
11.6%
s275376
11.2%
i273611
11.1%
a152488
 
6.2%
l142295
 
5.8%
m131939
 
5.4%
t127916
 
5.2%
g124370
 
5.0%
p110571
 
4.5%
Other values (12)535431
21.7%
Uppercase Letter
ValueCountFrequency (%)
A116021
32.3%
B89246
24.8%
E88255
24.5%
H12808
 
3.6%
T12149
 
3.4%
I11347
 
3.2%
K11158
 
3.1%
F10356
 
2.9%
U8291
 
2.3%
Space Separator
ValueCountFrequency (%)
79329
100.0%
Open Punctuation
ValueCountFrequency (%)
(12149
100.0%
Close Punctuation
ValueCountFrequency (%)
)12149
100.0%
Other Punctuation
ValueCountFrequency (%)
/98611
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2822569
93.3%
Common202238
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o303502
 
10.8%
n285439
 
10.1%
s275376
 
9.8%
i273611
 
9.7%
a152488
 
5.4%
l142295
 
5.0%
m131939
 
4.7%
t127916
 
4.5%
g124370
 
4.4%
A116021
 
4.1%
Other values (21)889612
31.5%
Common
ValueCountFrequency (%)
/98611
48.8%
79329
39.2%
(12149
 
6.0%
)12149
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3024807
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o303502
 
10.0%
n285439
 
9.4%
s275376
 
9.1%
i273611
 
9.0%
a152488
 
5.0%
l142295
 
4.7%
m131939
 
4.4%
t127916
 
4.2%
g124370
 
4.1%
A116021
 
3.8%
Other values (25)1091850
36.1%

Killed
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct205
Distinct (%)0.1%
Missing10313
Missing (%)5.7%
Infinite0
Infinite (%)0.0%
Mean2.403272299
Minimum0
Maximum1570
Zeros88149
Zeros (%)48.5%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-08-08T09:01:24.037781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile10
Maximum1570
Range1570
Interquartile range (IQR)2

Descriptive statistics

Standard deviation11.54574056
Coefficient of variation (CV)4.804174944
Kurtosis5578.043667
Mean2.403272299
Median Absolute Deviation (MAD)0
Skewness54.22986883
Sum411868
Variance133.3041251
MonotonicityNot monotonic
2021-08-08T09:01:24.287724image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
088149
48.5%
136576
20.1%
214147
 
7.8%
37738
 
4.3%
44961
 
2.7%
53565
 
2.0%
62552
 
1.4%
71960
 
1.1%
81495
 
0.8%
101192
 
0.7%
Other values (195)9043
 
5.0%
(Missing)10313
 
5.7%
ValueCountFrequency (%)
088149
48.5%
136576
20.1%
214147
 
7.8%
37738
 
4.3%
44961
 
2.7%
53565
 
2.0%
62552
 
1.4%
71960
 
1.1%
81495
 
0.8%
91054
 
0.6%
ValueCountFrequency (%)
15701
< 0.1%
13841
< 0.1%
13831
< 0.1%
11801
< 0.1%
9531
< 0.1%
6701
< 0.1%
5881
< 0.1%
5181
< 0.1%
5171
< 0.1%
4331
< 0.1%

Wounded
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct238
Distinct (%)0.1%
Missing16311
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean3.1676684
Minimum0
Maximum8191
Zeros103275
Zeros (%)56.8%
Negative0
Negative (%)0.0%
Memory size1.4 MiB
2021-08-08T09:01:24.614649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile13
Maximum8191
Range8191
Interquartile range (IQR)2

Descriptive statistics

Standard deviation35.94939181
Coefficient of variation (CV)11.34884946
Kurtosis36809.9728
Mean3.1676684
Median Absolute Deviation (MAD)0
Skewness174.7020782
Sum523869
Variance1292.358771
MonotonicityNot monotonic
2021-08-08T09:01:24.848972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0103275
56.8%
116033
 
8.8%
210219
 
5.6%
37303
 
4.0%
44880
 
2.7%
53820
 
2.1%
62856
 
1.6%
72435
 
1.3%
81821
 
1.0%
101379
 
0.8%
Other values (228)11359
 
6.3%
(Missing)16311
 
9.0%
ValueCountFrequency (%)
0103275
56.8%
116033
 
8.8%
210219
 
5.6%
37303
 
4.0%
44880
 
2.7%
53820
 
2.1%
62856
 
1.6%
72435
 
1.3%
81821
 
1.0%
8.52
 
< 0.1%
ValueCountFrequency (%)
81911
< 0.1%
81901
< 0.1%
55001
< 0.1%
40001
< 0.1%
15001
< 0.1%
12721
< 0.1%
10011
< 0.1%
8511
< 0.1%
8171
< 0.1%
8001
< 0.1%

Target
Categorical

HIGH CARDINALITY

Distinct86006
Distinct (%)47.5%
Missing636
Missing (%)0.4%
Memory size13.6 MiB
Civilians
 
6461
Unknown
 
5918
Soldiers
 
3157
Patrol
 
2942
Checkpoint
 
2905
Other values (86001)
159672 

Length

Max length343
Median length15
Mean length21.25532573
Min length1

Characters and Unicode

Total characters3848383
Distinct characters90
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78891 ?
Unique (%)43.6%

Sample

1st rowJulio Guzman
2nd rowNadine Chaval, daughter
3rd rowEmployee
4th rowU.S. Embassy
5th rowU.S. Consulate

Common Values

ValueCountFrequency (%)
Civilians6461
 
3.6%
Unknown5918
 
3.3%
Soldiers3157
 
1.7%
Patrol2942
 
1.6%
Checkpoint2905
 
1.6%
Vehicle2785
 
1.5%
Officers1787
 
1.0%
Village1679
 
0.9%
Military Unit1533
 
0.8%
Bus1335
 
0.7%
Other values (85996)150553
82.9%

Length

2021-08-08T09:01:25.488745image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of17732
 
3.1%
the15786
 
2.8%
in14131
 
2.5%
a12130
 
2.1%
civilians10528
 
1.8%
police8728
 
1.5%
unknown6316
 
1.1%
vehicle6170
 
1.1%
patrol5187
 
0.9%
was4963
 
0.9%
Other values (54887)468812
82.2%

Most occurring characters

ValueCountFrequency (%)
389816
 
10.1%
e317168
 
8.2%
a313650
 
8.2%
i311155
 
8.1%
n238188
 
6.2%
o228694
 
5.9%
r221911
 
5.8%
t202474
 
5.3%
l169749
 
4.4%
s158155
 
4.1%
Other values (80)1297423
33.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2976700
77.3%
Uppercase Letter395471
 
10.3%
Space Separator389816
 
10.1%
Other Punctuation54815
 
1.4%
Decimal Number12995
 
0.3%
Dash Punctuation8152
 
0.2%
Open Punctuation4991
 
0.1%
Close Punctuation4939
 
0.1%
Other Symbol410
 
< 0.1%
Math Symbol90
 
< 0.1%
Other values (2)4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e317168
10.7%
a313650
10.5%
i311155
10.5%
n238188
 
8.0%
o228694
 
7.7%
r221911
 
7.5%
t202474
 
6.8%
l169749
 
5.7%
s158155
 
5.3%
c111765
 
3.8%
Other values (17)703791
23.6%
Uppercase Letter
ValueCountFrequency (%)
C48705
12.3%
S38415
 
9.7%
P35367
 
8.9%
A35079
 
8.9%
M30714
 
7.8%
B21665
 
5.5%
T21455
 
5.4%
H15067
 
3.8%
R14885
 
3.8%
U14619
 
3.7%
Other values (16)119500
30.2%
Other Punctuation
ValueCountFrequency (%)
,22448
41.0%
:13690
25.0%
.12017
21.9%
'3019
 
5.5%
*1639
 
3.0%
/1238
 
2.3%
;228
 
0.4%
"227
 
0.4%
&217
 
0.4%
#50
 
0.1%
Other values (4)42
 
0.1%
Decimal Number
ValueCountFrequency (%)
22449
18.8%
12007
15.4%
31797
13.8%
41367
10.5%
51237
9.5%
01117
8.6%
6902
 
6.9%
7847
 
6.5%
8656
 
5.0%
9616
 
4.7%
Open Punctuation
ValueCountFrequency (%)
(4976
99.7%
[14
 
0.3%
{1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
+84
93.3%
=5
 
5.6%
>1
 
1.1%
Close Punctuation
ValueCountFrequency (%)
)4925
99.7%
]14
 
0.3%
Space Separator
ValueCountFrequency (%)
389816
100.0%
Dash Punctuation
ValueCountFrequency (%)
-8152
100.0%
Other Symbol
ValueCountFrequency (%)
410
100.0%
Currency Symbol
ValueCountFrequency (%)
$3
100.0%
Connector Punctuation
ValueCountFrequency (%)
_1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3372171
87.6%
Common476212
 
12.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
e317168
 
9.4%
a313650
 
9.3%
i311155
 
9.2%
n238188
 
7.1%
o228694
 
6.8%
r221911
 
6.6%
t202474
 
6.0%
l169749
 
5.0%
s158155
 
4.7%
c111765
 
3.3%
Other values (43)1099262
32.6%
Common
ValueCountFrequency (%)
389816
81.9%
,22448
 
4.7%
:13690
 
2.9%
.12017
 
2.5%
-8152
 
1.7%
(4976
 
1.0%
)4925
 
1.0%
'3019
 
0.6%
22449
 
0.5%
12007
 
0.4%
Other values (27)12713
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII3847971
> 99.9%
Specials410
 
< 0.1%
Latin 1 Sup2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
389816
 
10.1%
e317168
 
8.2%
a313650
 
8.2%
i311155
 
8.1%
n238188
 
6.2%
o228694
 
5.9%
r221911
 
5.8%
t202474
 
5.3%
l169749
 
4.4%
s158155
 
4.1%
Other values (78)1297011
33.7%
Specials
ValueCountFrequency (%)
410
100.0%
Latin 1 Sup
ValueCountFrequency (%)
é2
100.0%

Summary
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct112492
Distinct (%)97.3%
Missing66129
Missing (%)36.4%
Memory size43.4 MiB
09/00/2016: Sometime between September 18, 2016 and September 24, 2016, assailants detonated an explosive device at a civilian house in Dawr, Saladin, Iraq. There were no reported casualties resulting from the blast. No group claimed responsibility for the incident; however, sources attributed the attack to the Islamic State of Iraq and the Levant (ISIL).
 
100
12/01/2016: Assailants detonated explosive devices that destroyed an electricity transmission tower in Albu Bali, Al Anbar, Iraq. This was one of 80 similar attacks targeting towers that resulted in the death of two military engineering team members. No group claimed responsibility for the incidents.
 
80
05/29/2016: Security forces discovered and defused an explosives-rigged house in Sejar, Al Anbar, Iraq. No group claimed responsibility for the incident.
 
50
07/22/2016: Security forces discovered and defused an explosives-rigged house in Saqlawiyah, Al Anbar, Iraq. No group claimed responsibility for the incident.
 
36
01/11/2016: Assailants set fire to a shop in Muqdadiyah, Diyala governorate, Iraq. There were no reported casualties in the incident. This was one of 36 shops set alight and one of 51 attacks in and around Muqdadiyah on the same day. No group claimed responsibility; however, sources attributed the incidents to Asa'ib Ahl al-Haqq and the Badr Brigades.
 
36
Other values (112487)
115260 

Length

Max length2431
Median length277
Mean length298.6299822
Min length58

Characters and Unicode

Total characters34510278
Distinct characters90
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111316 ?
Unique (%)96.3%

Sample

1st row1/1/1970: Unknown African American assailants fired several bullets at police headquarters in Cairo, Illinois, United States. There were no casualties, however, one bullet narrowly missed several police officers. This attack took place during heightened racial tensions, including a Black boycott of White-owned businesses, in Cairo Illinois.
2nd row1/2/1970: Unknown perpetrators detonated explosives at the Pacific Gas & Electric Company Edes substation in Oakland, California, United States. Three transformers were damaged costing an estimated $20,000 to $25,000. There were no casualties.
3rd row1/2/1970: Karl Armstrong, a member of the New Years Gang, threw a firebomb at R.O.T.C. offices located within the Old Red Gym at the University of Wisconsin in Madison, Wisconsin, United States. There were no casualties but the fire caused around $60,000 in damages to the building.
4th row1/3/1970: Karl Armstrong, a member of the New Years Gang, broke into the University of Wisconsin's Primate Lab and set a fire on the first floor of the building. Armstrong intended to set fire to the Madison, Wisconsin, United States, Selective Service Headquarters across the street but mistakenly confused the building with the Primate Lab. The fire caused slight damages and was extinguished almost immediately.
5th row1/6/1970: Unknown perpetrators threw a Molotov cocktail into an Army Recruiting Station in Denver, Colorado, United States. There were no casualties but damages to the station were estimated at $305.

Common Values

ValueCountFrequency (%)
09/00/2016: Sometime between September 18, 2016 and September 24, 2016, assailants detonated an explosive device at a civilian house in Dawr, Saladin, Iraq. There were no reported casualties resulting from the blast. No group claimed responsibility for the incident; however, sources attributed the attack to the Islamic State of Iraq and the Levant (ISIL).100
 
0.1%
12/01/2016: Assailants detonated explosive devices that destroyed an electricity transmission tower in Albu Bali, Al Anbar, Iraq. This was one of 80 similar attacks targeting towers that resulted in the death of two military engineering team members. No group claimed responsibility for the incidents.80
 
< 0.1%
05/29/2016: Security forces discovered and defused an explosives-rigged house in Sejar, Al Anbar, Iraq. No group claimed responsibility for the incident.50
 
< 0.1%
07/22/2016: Security forces discovered and defused an explosives-rigged house in Saqlawiyah, Al Anbar, Iraq. No group claimed responsibility for the incident.36
 
< 0.1%
01/11/2016: Assailants set fire to a shop in Muqdadiyah, Diyala governorate, Iraq. There were no reported casualties in the incident. This was one of 36 shops set alight and one of 51 attacks in and around Muqdadiyah on the same day. No group claimed responsibility; however, sources attributed the incidents to Asa'ib Ahl al-Haqq and the Badr Brigades.36
 
< 0.1%
04/21/2017: Security forces discovered and defused an explosives-rigged house in Hayy ath-Thawrah neighborhood, Mosul, Nineveh, Iraq. This was one of 34 similar events in the area on this date. No group claimed responsibility for the incident.34
 
< 0.1%
06/05/2016: Security forces discovered and defused an explosives-rigged house in Rutbah, Al Anbar, Iraq. No group claimed responsibility for the incident.30
 
< 0.1%
08/08/2016: Security forces discovered and defused an explosives-rigged house in Khalidiyah, Al Anbar, Iraq. No group claimed responsibility for the incident.30
 
< 0.1%
10/23/2016: Security forces discovered and defused an explosives-rigged house in Dulab, Al Anbar, Iraq. No group claimed responsibility for the incident.27
 
< 0.1%
07/00/2014: Sometime between July 1 and July 14, 2014, an explosive device detonated at a private residence in Azim area, Diyala governorate, Iraq. There were no reported casualties; however, the house was damaged in the blast. This was one of 27 similar attacks on private residences in this area over a two week period. No group claimed responsibility for the incident; however, sources attributed the attack the to the Islamic State of Iraq and the Levant (ISIL).27
 
< 0.1%
Other values (112482)115112
63.4%
(Missing)66129
36.4%

Length

2021-08-08T09:01:26.051074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the371608
 
7.2%
in210955
 
4.1%
a119415
 
2.3%
of115610
 
2.2%
no112561
 
2.2%
and111649
 
2.2%
responsibility101733
 
2.0%
were99549
 
1.9%
claimed98412
 
1.9%
for91595
 
1.8%
Other values (102275)3744666
72.3%

Most occurring characters

ValueCountFrequency (%)
5079510
14.7%
e3062937
 
8.9%
a2508077
 
7.3%
i2381809
 
6.9%
t2127993
 
6.2%
n1920525
 
5.6%
o1778134
 
5.2%
r1645601
 
4.8%
s1560402
 
4.5%
d1215493
 
3.5%
Other values (80)11229797
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter25712078
74.5%
Space Separator5079510
 
14.7%
Uppercase Letter1375382
 
4.0%
Other Punctuation1147935
 
3.3%
Decimal Number1054543
 
3.1%
Dash Punctuation52711
 
0.2%
Close Punctuation41898
 
0.1%
Open Punctuation41896
 
0.1%
Other Symbol3627
 
< 0.1%
Currency Symbol691
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e3062937
11.9%
a2508077
 
9.8%
i2381809
 
9.3%
t2127993
 
8.3%
n1920525
 
7.5%
o1778134
 
6.9%
r1645601
 
6.4%
s1560402
 
6.1%
d1215493
 
4.7%
l1153359
 
4.5%
Other values (20)6357748
24.7%
Uppercase Letter
ValueCountFrequency (%)
A220453
16.0%
T141962
10.3%
N138757
10.1%
S113591
 
8.3%
I102201
 
7.4%
P80775
 
5.9%
M74065
 
5.4%
B69308
 
5.0%
C50066
 
3.6%
K48017
 
3.5%
Other values (16)336187
24.4%
Other Punctuation
ValueCountFrequency (%)
,392911
34.2%
.354743
30.9%
/233596
20.3%
:116698
 
10.2%
;31174
 
2.7%
'15936
 
1.4%
"2720
 
0.2%
&79
 
< 0.1%
!40
 
< 0.1%
#18
 
< 0.1%
Other values (3)20
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0318300
30.2%
1227221
21.5%
2210249
19.9%
349437
 
4.7%
548585
 
4.6%
447625
 
4.5%
642835
 
4.1%
741909
 
4.0%
936160
 
3.4%
832222
 
3.1%
Math Symbol
ValueCountFrequency (%)
+5
71.4%
~1
 
14.3%
=1
 
14.3%
Open Punctuation
ValueCountFrequency (%)
(41773
99.7%
[123
 
0.3%
Close Punctuation
ValueCountFrequency (%)
)41772
99.7%
]126
 
0.3%
Space Separator
ValueCountFrequency (%)
5079510
100.0%
Dash Punctuation
ValueCountFrequency (%)
-52711
100.0%
Currency Symbol
ValueCountFrequency (%)
$691
100.0%
Other Symbol
ValueCountFrequency (%)
3627
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin27087460
78.5%
Common7422818
 
21.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e3062937
 
11.3%
a2508077
 
9.3%
i2381809
 
8.8%
t2127993
 
7.9%
n1920525
 
7.1%
o1778134
 
6.6%
r1645601
 
6.1%
s1560402
 
5.8%
d1215493
 
4.5%
l1153359
 
4.3%
Other values (46)7733130
28.5%
Common
ValueCountFrequency (%)
5079510
68.4%
,392911
 
5.3%
.354743
 
4.8%
0318300
 
4.3%
/233596
 
3.1%
1227221
 
3.1%
2210249
 
2.8%
:116698
 
1.6%
-52711
 
0.7%
349437
 
0.7%
Other values (24)387442
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII34506641
> 99.9%
Specials3627
 
< 0.1%
Latin 1 Sup10
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5079510
14.7%
e3062937
 
8.9%
a2508077
 
7.3%
i2381809
 
6.9%
t2127993
 
6.2%
n1920525
 
5.6%
o1778134
 
5.2%
r1645601
 
4.8%
s1560402
 
4.5%
d1215493
 
3.5%
Other values (75)11226160
32.5%
Specials
ValueCountFrequency (%)
3627
100.0%
Latin 1 Sup
ValueCountFrequency (%)
ñ3
30.0%
ó3
30.0%
á2
20.0%
í2
20.0%

Group
Categorical

HIGH CARDINALITY

Distinct3537
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size13.0 MiB
Unknown
82782 
Taliban
 
7478
Islamic State of Iraq and the Levant (ISIL)
 
5613
Shining Path (SL)
 
4555
Farabundo Marti National Liberation Front (FMLN)
 
3351
Other values (3532)
77912 

Length

Max length112
Median length7
Mean length17.93960075
Min length2

Characters and Unicode

Total characters3259464
Distinct characters74
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1711 ?
Unique (%)0.9%

Sample

1st rowMANO-D
2nd row23rd of September Communist League
3rd rowUnknown
4th rowUnknown
5th rowUnknown

Common Values

ValueCountFrequency (%)
Unknown82782
45.6%
Taliban7478
 
4.1%
Islamic State of Iraq and the Levant (ISIL)5613
 
3.1%
Shining Path (SL)4555
 
2.5%
Farabundo Marti National Liberation Front (FMLN)3351
 
1.8%
Al-Shabaab3288
 
1.8%
New People's Army (NPA)2772
 
1.5%
Irish Republican Army (IRA)2671
 
1.5%
Revolutionary Armed Forces of Colombia (FARC)2487
 
1.4%
Boko Haram2418
 
1.3%
Other values (3527)64276
35.4%

Length

2021-08-08T09:01:26.613441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
unknown82782
 
18.0%
of19956
 
4.3%
liberation12948
 
2.8%
the10937
 
2.4%
army10528
 
2.3%
islamic9977
 
2.2%
front9559
 
2.1%
national9129
 
2.0%
and8250
 
1.8%
taliban7520
 
1.6%
Other values (4088)279290
60.6%

Most occurring characters

ValueCountFrequency (%)
n393075
 
12.1%
279191
 
8.6%
a244138
 
7.5%
o229005
 
7.0%
i179789
 
5.5%
e157318
 
4.8%
t152270
 
4.7%
r139909
 
4.3%
k95730
 
2.9%
s94275
 
2.9%
Other values (64)1294764
39.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2283218
70.0%
Uppercase Letter548326
 
16.8%
Space Separator279191
 
8.6%
Open Punctuation57645
 
1.8%
Close Punctuation57645
 
1.8%
Dash Punctuation20762
 
0.6%
Other Punctuation9115
 
0.3%
Decimal Number3555
 
0.1%
Other Symbol7
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U88010
16.1%
A52909
9.6%
I44461
 
8.1%
L43829
 
8.0%
P40340
 
7.4%
F39063
 
7.1%
S36520
 
6.7%
M31338
 
5.7%
N29184
 
5.3%
T27827
 
5.1%
Other values (16)114845
20.9%
Lowercase Letter
ValueCountFrequency (%)
n393075
17.2%
a244138
10.7%
o229005
10.0%
i179789
 
7.9%
e157318
 
6.9%
t152270
 
6.7%
r139909
 
6.1%
k95730
 
4.2%
s94275
 
4.1%
l92343
 
4.0%
Other values (16)505366
22.1%
Decimal Number
ValueCountFrequency (%)
11515
42.6%
91167
32.8%
7321
 
9.0%
2207
 
5.8%
3105
 
3.0%
563
 
1.8%
661
 
1.7%
051
 
1.4%
843
 
1.2%
422
 
0.6%
Other Punctuation
ValueCountFrequency (%)
'8801
96.6%
/149
 
1.6%
,96
 
1.1%
.26
 
0.3%
"20
 
0.2%
!13
 
0.1%
:10
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
-20762
100.0%
Space Separator
ValueCountFrequency (%)
279191
100.0%
Open Punctuation
ValueCountFrequency (%)
(57645
100.0%
Close Punctuation
ValueCountFrequency (%)
)57645
100.0%
Other Symbol
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2831544
86.9%
Common427920
 
13.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n393075
 
13.9%
a244138
 
8.6%
o229005
 
8.1%
i179789
 
6.3%
e157318
 
5.6%
t152270
 
5.4%
r139909
 
4.9%
k95730
 
3.4%
s94275
 
3.3%
l92343
 
3.3%
Other values (42)1053692
37.2%
Common
ValueCountFrequency (%)
279191
65.2%
(57645
 
13.5%
)57645
 
13.5%
-20762
 
4.9%
'8801
 
2.1%
11515
 
0.4%
91167
 
0.3%
7321
 
0.1%
2207
 
< 0.1%
/149
 
< 0.1%
Other values (12)517
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII3259457
> 99.9%
Specials7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n393075
 
12.1%
279191
 
8.6%
a244138
 
7.5%
o229005
 
7.0%
i179789
 
5.5%
e157318
 
4.8%
t152270
 
4.7%
r139909
 
4.3%
k95730
 
2.9%
s94275
 
2.9%
Other values (63)1294757
39.7%
Specials
ValueCountFrequency (%)
7
100.0%

Target_type
Categorical

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.6 MiB
Private Citizens & Property
43511 
Military
27984 
Police
24506 
Government (General)
21283 
Business
20669 
Other values (17)
43738 

Length

Max length30
Median length14
Mean length15.90251581
Min length3

Characters and Unicode

Total characters2889344
Distinct characters48
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPrivate Citizens & Property
2nd rowGovernment (Diplomatic)
3rd rowJournalists & Media
4th rowGovernment (Diplomatic)
5th rowGovernment (Diplomatic)

Common Values

ValueCountFrequency (%)
Private Citizens & Property43511
23.9%
Military27984
15.4%
Police24506
13.5%
Government (General)21283
11.7%
Business20669
11.4%
Transportation6799
 
3.7%
Utilities6023
 
3.3%
Unknown5898
 
3.2%
Religious Figures/Institutions4440
 
2.4%
Educational Institution4322
 
2.4%
Other values (12)16256
 
8.9%

Length

2021-08-08T09:01:27.269537image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
47802
13.2%
property43511
12.0%
citizens43511
12.0%
private43511
12.0%
military27984
7.7%
government24856
6.9%
police24506
6.8%
general21283
 
5.9%
business20669
 
5.7%
transportation6799
 
1.9%
Other values (27)57977
16.0%

Most occurring characters

ValueCountFrequency (%)
i328615
11.4%
e297130
 
10.3%
t269366
 
9.3%
r243822
 
8.4%
n198447
 
6.9%
180718
 
6.3%
s154619
 
5.4%
o151502
 
5.2%
a137582
 
4.8%
P115260
 
4.0%
Other values (38)812283
28.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2273846
78.7%
Uppercase Letter326748
 
11.3%
Space Separator180718
 
6.3%
Other Punctuation55281
 
1.9%
Open Punctuation24856
 
0.9%
Close Punctuation24856
 
0.9%
Dash Punctuation3039
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i328615
14.5%
e297130
13.1%
t269366
11.8%
r243822
10.7%
n198447
8.7%
s154619
6.8%
o151502
6.7%
a137582
6.1%
l105305
 
4.6%
y73678
 
3.2%
Other values (13)313780
13.8%
Uppercase Letter
ValueCountFrequency (%)
P115260
35.3%
G47109
14.4%
C43511
 
13.3%
M34322
 
10.5%
B20669
 
6.3%
U11921
 
3.6%
T11287
 
3.5%
I8762
 
2.7%
F4757
 
1.5%
R4703
 
1.4%
Other values (9)24447
 
7.5%
Other Punctuation
ValueCountFrequency (%)
&47802
86.5%
/7479
 
13.5%
Space Separator
ValueCountFrequency (%)
180718
100.0%
Open Punctuation
ValueCountFrequency (%)
(24856
100.0%
Close Punctuation
ValueCountFrequency (%)
)24856
100.0%
Dash Punctuation
ValueCountFrequency (%)
-3039
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2600594
90.0%
Common288750
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i328615
12.6%
e297130
11.4%
t269366
10.4%
r243822
9.4%
n198447
 
7.6%
s154619
 
5.9%
o151502
 
5.8%
a137582
 
5.3%
P115260
 
4.4%
l105305
 
4.0%
Other values (32)598946
23.0%
Common
ValueCountFrequency (%)
180718
62.6%
&47802
 
16.6%
(24856
 
8.6%
)24856
 
8.6%
/7479
 
2.6%
-3039
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2889344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i328615
11.4%
e297130
 
10.3%
t269366
 
9.3%
r243822
 
8.4%
n198447
 
6.9%
180718
 
6.3%
s154619
 
5.4%
o151502
 
5.2%
a137582
 
4.8%
P115260
 
4.0%
Other values (38)812283
28.1%

Weapon_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.4 MiB
Explosives
92426 
Firearms
58524 
Unknown
15157 
Incendiary
11135 
Melee
 
3655
Other values (7)
 
794

Length

Max length75
Median length10
Mean length9.053646025
Min length5

Characters and Unicode

Total characters1644966
Distinct characters41
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowExplosives
5th rowIncendiary

Common Values

ValueCountFrequency (%)
Explosives92426
50.9%
Firearms58524
32.2%
Unknown15157
 
8.3%
Incendiary11135
 
6.1%
Melee3655
 
2.0%
Chemical321
 
0.2%
Sabotage Equipment141
 
0.1%
Vehicle (not to include vehicle-borne explosives, i.e., car or truck bombs)136
 
0.1%
Other114
 
0.1%
Biological35
 
< 0.1%
Other values (2)47
 
< 0.1%

Length

2021-08-08T09:01:27.706937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
explosives92562
50.5%
firearms58524
31.9%
unknown15157
 
8.3%
incendiary11135
 
6.1%
melee3655
 
2.0%
chemical321
 
0.2%
equipment141
 
0.1%
sabotage141
 
0.1%
vehicle136
 
0.1%
to136
 
0.1%
Other values (13)1317
 
0.7%

Most occurring characters

ValueCountFrequency (%)
s243817
14.8%
e175057
10.6%
i163325
9.9%
r128841
 
7.8%
o108671
 
6.6%
l97044
 
5.9%
p92736
 
5.6%
v92698
 
5.6%
E92567
 
5.6%
x92562
 
5.6%
Other values (31)357648
21.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1460615
88.8%
Uppercase Letter181865
 
11.1%
Space Separator1534
 
0.1%
Other Punctuation544
 
< 0.1%
Open Punctuation136
 
< 0.1%
Dash Punctuation136
 
< 0.1%
Close Punctuation136
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s243817
16.7%
e175057
12.0%
i163325
11.2%
r128841
8.8%
o108671
7.4%
l97044
 
6.6%
p92736
 
6.3%
v92698
 
6.3%
x92562
 
6.3%
a70527
 
4.8%
Other values (13)195337
13.4%
Uppercase Letter
ValueCountFrequency (%)
E92567
50.9%
F58557
32.2%
U15157
 
8.3%
I11135
 
6.1%
M3655
 
2.0%
C321
 
0.2%
S141
 
0.1%
V136
 
0.1%
O114
 
0.1%
B35
 
< 0.1%
Other values (2)47
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
,272
50.0%
.272
50.0%
Space Separator
ValueCountFrequency (%)
1534
100.0%
Open Punctuation
ValueCountFrequency (%)
(136
100.0%
Dash Punctuation
ValueCountFrequency (%)
-136
100.0%
Close Punctuation
ValueCountFrequency (%)
)136
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1642480
99.8%
Common2486
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
s243817
14.8%
e175057
10.7%
i163325
9.9%
r128841
 
7.8%
o108671
 
6.6%
l97044
 
5.9%
p92736
 
5.6%
v92698
 
5.6%
E92567
 
5.6%
x92562
 
5.6%
Other values (25)355162
21.6%
Common
ValueCountFrequency (%)
1534
61.7%
,272
 
10.9%
.272
 
10.9%
(136
 
5.5%
-136
 
5.5%
)136
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII1644966
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s243817
14.8%
e175057
10.6%
i163325
9.9%
r128841
 
7.8%
o108671
 
6.6%
l97044
 
5.9%
p92736
 
5.6%
v92698
 
5.6%
E92567
 
5.6%
x92562
 
5.6%
Other values (31)357648
21.7%

Motive
Categorical

HIGH CARDINALITY
MISSING

Distinct14490
Distinct (%)28.7%
Missing131130
Missing (%)72.2%
Memory size10.8 MiB
Unknown
14889 
The specific motive for the attack is unknown.
14430 
The specific motive is unknown; however, sources noted that the attack may have been part of a larger trend of violence related to Bangladesh's nationwide hartal, which began on January 6, 2015.
 
297
The specific motive for the attack is unknown..
 
148
The specific motive for the attack is unknown or was not reported.
 
144
Other values (14485)
20653 

Length

Max length899
Median length46
Mean length82.93038112
Min length1

Characters and Unicode

Total characters4193043
Distinct characters82
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12579 ?
Unique (%)24.9%

Sample

1st rowTo protest the Cairo Illinois Police Deparment
2nd rowTo protest the War in Vietnam and the draft
3rd rowTo protest the War in Vietnam and the draft
4th rowProtest the draft and Vietnam War
5th rowTo protest United States owned businesses in Puerto Rico

Common Values

ValueCountFrequency (%)
Unknown14889
 
8.2%
The specific motive for the attack is unknown.14430
 
7.9%
The specific motive is unknown; however, sources noted that the attack may have been part of a larger trend of violence related to Bangladesh's nationwide hartal, which began on January 6, 2015.297
 
0.2%
The specific motive for the attack is unknown..148
 
0.1%
The specific motive for the attack is unknown or was not reported.144
 
0.1%
The specific motive for the attack is unknown73
 
< 0.1%
Part of a campaign by Islamic extremists to destabilize Algeria by weakening security forces protecting the 'apostate' Algerian government.66
 
< 0.1%
The specific motive for the attack was political.57
 
< 0.1%
The specific motive is unknown; however, sources suspected that the attack, which targeted members of the Sunni community, may have been part of a larger trend of sectarian violence between Iraq's minority Sunni and majority Shiite communities.52
 
< 0.1%
The specific motive is unknown; however, sources noted that parliamentary elections were scheduled for January 5, 2014. Sources also noted that the opposition advocated for citizens to "resist" the elections.50
 
< 0.1%
Other values (14480)20355
 
11.2%
(Missing)131130
72.2%

Length

2021-08-08T09:01:28.316164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the84872
 
12.8%
unknown40952
 
6.2%
is27336
 
4.1%
motive27291
 
4.1%
specific26968
 
4.1%
attack25416
 
3.8%
for24764
 
3.7%
of17704
 
2.7%
that15996
 
2.4%
to13765
 
2.1%
Other values (15713)360167
54.1%

Most occurring characters

ValueCountFrequency (%)
615819
14.7%
e399648
 
9.5%
t339074
 
8.1%
i291715
 
7.0%
n278822
 
6.6%
a267120
 
6.4%
o252004
 
6.0%
s199879
 
4.8%
r173463
 
4.1%
c161332
 
3.8%
Other values (72)1214167
29.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter3338439
79.6%
Space Separator615819
 
14.7%
Uppercase Letter134683
 
3.2%
Other Punctuation78267
 
1.9%
Decimal Number10834
 
0.3%
Dash Punctuation5506
 
0.1%
Close Punctuation4653
 
0.1%
Open Punctuation4635
 
0.1%
Other Symbol178
 
< 0.1%
Currency Symbol28
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T34987
26.0%
U16773
12.5%
S12693
 
9.4%
A10793
 
8.0%
I9455
 
7.0%
P7383
 
5.5%
M7060
 
5.2%
L4253
 
3.2%
N4115
 
3.1%
C3976
 
3.0%
Other values (16)23195
17.2%
Lowercase Letter
ValueCountFrequency (%)
e399648
12.0%
t339074
10.2%
i291715
 
8.7%
n278822
 
8.4%
a267120
 
8.0%
o252004
 
7.5%
s199879
 
6.0%
r173463
 
5.2%
c161332
 
4.8%
h152081
 
4.6%
Other values (16)823301
24.7%
Other Punctuation
ValueCountFrequency (%)
.37066
47.4%
,21860
27.9%
;10576
 
13.5%
'5726
 
7.3%
"2754
 
3.5%
/162
 
0.2%
:80
 
0.1%
!20
 
< 0.1%
?15
 
< 0.1%
&5
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
12495
23.0%
02462
22.7%
22313
21.3%
5721
 
6.7%
3692
 
6.4%
6642
 
5.9%
4543
 
5.0%
7385
 
3.6%
9301
 
2.8%
8280
 
2.6%
Close Punctuation
ValueCountFrequency (%)
)4600
98.9%
]53
 
1.1%
Open Punctuation
ValueCountFrequency (%)
(4582
98.9%
[53
 
1.1%
Space Separator
ValueCountFrequency (%)
615819
100.0%
Dash Punctuation
ValueCountFrequency (%)
-5506
100.0%
Other Symbol
ValueCountFrequency (%)
178
100.0%
Currency Symbol
ValueCountFrequency (%)
$28
100.0%
Math Symbol
ValueCountFrequency (%)
+1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3473122
82.8%
Common719921
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e399648
11.5%
t339074
 
9.8%
i291715
 
8.4%
n278822
 
8.0%
a267120
 
7.7%
o252004
 
7.3%
s199879
 
5.8%
r173463
 
5.0%
c161332
 
4.6%
h152081
 
4.4%
Other values (42)957984
27.6%
Common
ValueCountFrequency (%)
615819
85.5%
.37066
 
5.1%
,21860
 
3.0%
;10576
 
1.5%
'5726
 
0.8%
-5506
 
0.8%
)4600
 
0.6%
(4582
 
0.6%
"2754
 
0.4%
12495
 
0.3%
Other values (20)8937
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII4192865
> 99.9%
Specials178
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
615819
14.7%
e399648
 
9.5%
t339074
 
8.1%
i291715
 
7.0%
n278822
 
6.6%
a267120
 
6.4%
o252004
 
6.0%
s199879
 
4.8%
r173463
 
4.1%
c161332
 
3.8%
Other values (71)1213989
29.0%
Specials
ValueCountFrequency (%)
178
100.0%

Interactions

2021-08-08T09:00:49.906731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:50.765627image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:51.093673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:51.437613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:51.781243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:52.124914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:52.452998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:52.781011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:53.077820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:53.405900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:53.765151image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:54.093201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:54.390008image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:54.733673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:55.046099image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:55.389768image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:55.702231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:56.030246image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:56.373913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:56.702045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:57.074881image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:57.431815image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:57.775446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:58.094614image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:58.422661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:58.781994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:59.078754image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:59.406842image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:00:59.719230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:00.031656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:00.375324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:00.687790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:01.000177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:01.312607image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:01.874972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:02.437339image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:02.984086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:03.687048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:04.093199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:04.421247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:06.716767image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:07.128219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:07.460578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:07.810818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:08.109553image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:08.454485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:08.760365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:09.041510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-08-08T09:01:09.353937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-08-08T09:01:28.581769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-08T09:01:28.894160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-08T09:01:29.222207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-08T09:01:29.534668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-08-08T09:01:29.878341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-08-08T09:01:11.926876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-08T09:01:13.473386image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-08-08T09:01:15.660375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-08-08T09:01:16.488303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

YearMonthDayCountrystateRegioncitylatitudelongitudeAttackTypeKilledWoundedTargetSummaryGroupTarget_typeWeapon_typeMotive
0197072Dominican RepublicNaNCentral America & CaribbeanSanto Domingo18.456792-69.951164Assassination1.00.0Julio GuzmanNaNMANO-DPrivate Citizens & PropertyUnknownNaN
1197000MexicoFederalNorth AmericaMexico city19.371887-99.086624Hostage Taking (Kidnapping)0.00.0Nadine Chaval, daughterNaN23rd of September Communist LeagueGovernment (Diplomatic)UnknownNaN
2197010PhilippinesTarlacSoutheast AsiaUnknown15.478598120.599741Assassination1.00.0EmployeeNaNUnknownJournalists & MediaUnknownNaN
3197010GreeceAtticaWestern EuropeAthens37.99749023.762728Bombing/ExplosionNaNNaNU.S. EmbassyNaNUnknownGovernment (Diplomatic)ExplosivesNaN
4197010JapanFukoukaEast AsiaFukouka33.580412130.396361Facility/Infrastructure AttackNaNNaNU.S. ConsulateNaNUnknownGovernment (Diplomatic)IncendiaryNaN
5197011United StatesIllinoisNorth AmericaCairo37.005105-89.176269Armed Assault0.00.0Cairo Police Headquarters1/1/1970: Unknown African American assailants fired several bullets at police headquarters in Cairo, Illinois, United States. There were no casualties, however, one bullet narrowly missed several police officers. This attack took place during heightened racial tensions, including a Black boycott of White-owned businesses, in Cairo Illinois.Black NationalistsPoliceFirearmsTo protest the Cairo Illinois Police Deparment
6197012UruguayMontevideoSouth AmericaMontevideo-34.891151-56.187214Assassination0.00.0Juan Maria de Lucah/Chief of Directorate of info. and intell.NaNTupamaros (Uruguay)PoliceFirearmsNaN
7197012United StatesCaliforniaNorth AmericaOakland37.791927-122.225906Bombing/Explosion0.00.0Edes Substation1/2/1970: Unknown perpetrators detonated explosives at the Pacific Gas & Electric Company Edes substation in Oakland, California, United States. Three transformers were damaged costing an estimated $20,000 to $25,000. There were no casualties.UnknownUtilitiesExplosivesNaN
8197012United StatesWisconsinNorth AmericaMadison43.076592-89.412488Facility/Infrastructure Attack0.00.0R.O.T.C. offices at University of Wisconsin, Madison1/2/1970: Karl Armstrong, a member of the New Years Gang, threw a firebomb at R.O.T.C. offices located within the Old Red Gym at the University of Wisconsin in Madison, Wisconsin, United States. There were no casualties but the fire caused around $60,000 in damages to the building.New Year's GangMilitaryIncendiaryTo protest the War in Vietnam and the draft
9197013United StatesWisconsinNorth AmericaMadison43.072950-89.386694Facility/Infrastructure Attack0.00.0Selective Service Headquarters in Madison Wisconsin1/3/1970: Karl Armstrong, a member of the New Years Gang, broke into the University of Wisconsin's Primate Lab and set a fire on the first floor of the building. Armstrong intended to set fire to the Madison, Wisconsin, United States, Selective Service Headquarters across the street but mistakenly confused the building with the Primate Lab. The fire caused slight damages and was extinguished almost immediately.New Year's GangGovernment (General)IncendiaryTo protest the War in Vietnam and the draft

Last rows

YearMonthDayCountrystateRegioncitylatitudelongitudeAttackTypeKilledWoundedTargetSummaryGroupTarget_typeWeapon_typeMotive
18168120171231PhilippinesMaguindanaoSoutheast AsiaShariff Aguak6.862806124.443649Bombing/Explosion1.05.0Patrol12/31/2017: A roadside bomb detonated targeting a police vehicle in Poblacion Mother, Tuayan, Shariff Aguak, Maguindanao, Philippines. A police officer was killed and five others were injured in the blast. No group claimed responsibility for the incident; however, sources attributed the attack to the Bangsamoro Islamic Freedom Movement (BIFM).Bangsamoro Islamic Freedom Movement (BIFM)PoliceExplosivesNaN
18168220171231ItalyMarcheWestern EuropeSpinetoli42.88898213.772795Facility/Infrastructure Attack0.00.0Migrant Center12/31/2017: Assailants set fire to a migrants' center in Spinetoli, Marche, Italy. There were no reported casualties. No group claimed responsibility for the incident.UnknownPrivate Citizens & PropertyIncendiaryThe specific motive is unknown; however, sources noted that the targeted building housed migrants.
18168320171231AfghanistanFaryabSouth AsiaKohistan district35.31546764.815508Armed Assault6.00.0House of Member12/31/2017: Assailants attacked the house of a public uprising member in Kohistan district, Faryab, Afghanistan. Six relatives of the uprising member were killed in the attack. No group claimed responsibility for the incident; however, sources attributed the attack to the Taliban.TalibanTerrorists/Non-State MilitiaFirearmsThe specific motive is unknown; however, sources stated that the Taliban had warned one of the victims against collaborating with the government.
18168420171231IndiaAssamSouth AsiaHungrum25.18016293.015788Hostage Taking (Kidnapping)0.00.0Personal Security Officer of Council Member Ihuing Pame: Prafulla Phukan12/31/2017: Assailants abducted Prafulla Phukan, a personal security officer (PSO), from Hungrum, Assam, India. Phukan was rescued by security forces the following morning. No group claimed responsibility for the incident; however, sources attributed the attack to the Zeliangrong United Front.Zeliangrong United FrontGovernment (General)FirearmsNaN
18168520171231AfghanistanFaryabSouth AsiaMaymana35.92105164.774544Bombing/Explosion0.04.0Jewelry Shop12/31/2017: An explosive device detonated at a jewelry shop in Maymana, Faryab, Afghanistan. Four people were injured in the blast. No group claimed responsibility for the incident.UnknownBusinessExplosivesNaN
18168620171231SomaliaMiddle ShebelleSub-Saharan AfricaCeelka Geelow2.35967345.385034Armed Assault1.02.0Checkpoint12/31/2017: Assailants opened fire on a Somali National Army (SNA) checkpoint in Ceelka Geelow, Middle Shebelle, Somalia. At least one soldier was killed and two soldiers were injured in the ensuing clash. Al-Shabaab claimed responsibility for the attack.Al-ShabaabMilitaryFirearmsNaN
18168720171231SyriaLattakiaMiddle East & North AfricaJableh35.40727835.942679Bombing/Explosion2.07.0Hmeymim Air Base12/31/2017: Assailants launched mortars at the Hmeymim Air Base in Jableh, Lattakia, Syria. Two Russian soldiers were killed and ten people were injured in the attack. No group claimed responsibility for the incident; however, sources attributed the attack to Muslim extremists.Muslim extremistsMilitaryExplosivesNaN
18168820171231PhilippinesMaguindanaoSoutheast AsiaKubentog6.900742124.437908Facility/Infrastructure Attack0.00.0Houses12/31/2017: Assailants set fire to houses in Kubentog, Datu Hoffer, Maguindanao, Philippines. There were no reported casualties in the attack. No group claimed responsibility for the incident; however, sources attributed the attack to the Bangsamoro Islamic Freedom Movement (BIFM).Bangsamoro Islamic Freedom Movement (BIFM)Private Citizens & PropertyIncendiaryNaN
18168920171231IndiaManipurSouth AsiaImphal24.79834693.940430Bombing/Explosion0.00.0Office12/31/2017: Assailants threw a grenade at a Forest Department office in Mantripukhri neighborhood, Imphal, Manipur, India. No casualties were reported in the blast. No group claimed responsibility for the incident.UnknownGovernment (General)ExplosivesNaN
18169020171231PhilippinesMaguindanaoSoutheast AsiaCotabato City7.209594124.241966Bombing/Explosion0.00.0Unknown12/31/2017: An explosive device was discovered and defused at a plaza in Cotabato City, Maguindanao, Philippines. No group claimed responsibility for the incident.UnknownUnknownExplosivesNaN

Duplicate rows

Most frequently occurring

YearMonthDayCountrystateRegioncitylatitudelongitudeAttackTypeKilledWoundedTargetSummaryGroupTarget_typeWeapon_typeMotive# duplicates
1912016111IraqDiyalaMiddle East & North AfricaMuqdadiyah33.95316744.921906Facility/Infrastructure Attack0.00.0Shop01/11/2016: Assailants set fire to a shop in Muqdadiyah, Diyala governorate, Iraq. There were no reported casualties in the incident. This was one of 36 shops set alight and one of 51 attacks in and around Muqdadiyah on the same day. No group claimed responsibility; however, sources attributed the incidents to Asa'ib Ahl al-Haqq and the Badr Brigades.Asa'ib Ahl al-HaqqBusinessIncendiaryThe specific motive is unknown; however, sources suspected that the attack, which targeted members of the Sunni community, may have been part of a larger trend of sectarian violence between Iraq's minority Sunni and majority Shiite communities.36
932012511FranceCorsicaWestern EuropeUnknown41.9188918.737554Bombing/Explosion0.00.0Vacation Homes of Foreigners05/11/2012: Assailants detonated an explosive device in a holiday home in Corsica region, France. This was one of 26 attacks on vacation homes throughout the island on May 11, 2012. The Corsican National Liberation Front (FLNC) claimed responsibility for the incidents, stating they carried out the attack to fight property speculation.Corsican National Liberation Front (FLNC)Private Citizens & PropertyExplosivesThe specific motive is unknown; however, a source noted that the Corsican National Liberation Front (FLNC) had claimed to be fighting land development, which it views as a threat to the Corsican people. The group frequently targets properties belonging to foreigners and mainland French citizens.25
1092012128FranceCorsicaWestern EuropeUnknown41.9188918.737554Bombing/Explosion0.00.0Vacation Homes12/08/2012: Assailants detonated an explosive device at an under-construction vacation home in Corsica region, France. This was one of 24 similar attacks on the same night. There were no reported casualties; however, 26 houses were destroyed in the blasts. The Corsican National Liberation Front (FLNC) claimed responsibility for the incident by writing the group name on the walls of one of the targeted houses.Corsican National Liberation Front (FLNC)Private Citizens & PropertyExplosivesThe specific motive is unknown; however, sources noted that the Corsican National Liberation Front (FLNC) has previously targeted holiday homes, claiming that these structures are driving up housing prices and destroying the coastline. Moreover, sources speculated that Corsican nationalism may have been a motivation, noting that the targeted homes belonged to non-Corsicans and that the bombings occurred just before Corsica's National Identity Day.24
1542014614AfghanistanNangarharSouth AsiaUnknown34.17183170.621679Facility/Infrastructure Attack0.00.0Polling Station06/14/2014: Assailants attacked a polling station in Nangarhar province, Afghanistan. There were no reported casualties in the attack. This was one of 19 attacks on polling stations in Nangarhar province on June 14, 2014. No group claimed responsibility for the incident.UnknownGovernment (General)UnknownThe specific motive is unknown; however, sources noted that the attack on the polling station was meant to disrupt the run-off elections occurring on June 14, 2014.18
1262013109IraqKirkukMiddle East & North AfricaHawijah district35.32378943.772096Bombing/Explosion0.00.0House of Officer10/09/2013: Assailants detonated at least one explosive device at the home of a police officer in Hawijah district, Kirkuk, Iraq. This was one of eighteen bombings that occurred at the homes of security personnel in one of three villages in Kirkuk on this day. There were no reported casualties resulting from the attacks, although the buildings were damaged. No group claimed responsibility; however, sources suspect that Islamic State of Iraq and the Levant (ISIL) was behind the incident.Islamic State of Iraq and the Levant (ISIL)PoliceExplosivesThe specific motive is unknown; however, sources noted that the affected areas had witnessed a sharp increase in attacks on houses belonging to the army and police forces after Al-Qa ida distributed leaflets calling for security forces to leave their jobs or their homes will be blown up.17
572008729IndiaGujaratSouth AsiaSurat21.22049672.831058Bombing/Explosion0.00.0Civilians were the target in the attack.07/29/2008: On Tuesday, a bomb was placed in the Katargam area in Surat, but police found and defused it before it detonated. No casualties were reported. No claim of responsibility has been reported.UnknownPrivate Citizens & PropertyExplosivesThe specific motive for the attack is unknown.13
94201269IndiaOdishaSouth AsiaUnknown20.23755684.270018Facility/Infrastructure Attack0.00.0Tendu Leaf Warehouse06/09/2012: Assailants set fire to a government-run tendu leaf warehouse in Odisha state, India. There were no reported injuries in the incident, although the warehouse was damaged. This was one of 13 similar attacks on warehouses in a 24-hour period in Singhbahali, Bharuamunda and Badtia villages, located in Balangir and Nuapada districts. Communist Party of India: Maoist (CPI-M) claimed responsibility for the incident; they left behind pamphlets listing eight demands, including a raise in the price of kendu leaves and higher compensation for leaf harvesters.Communist Party of India - Maoist (CPI-Maoist)Government (General)IncendiaryMaoists left behind pamphlets listing eight demands of the government, including a raise in the price of kendu leaves and higher compensation for leaf harvesters.13
32200311ColombiaPutumayoSouth AmericaPuerto Colon0.280373-76.926854Bombing/Explosion0.00.0Trans-Andean Pipeline between the villages of Puerto Colon and San Miguel, and in the Balestra region01/01/2003: Guerrillas of the 48th Front of the Fuerzas Armadas Revolucionarias de Colombia (FARC) were accused of planting eleven explosive charges on the Trans-Andean Pipeline between the villages of Puerto Colon and San Miguel, and in the Balestra region. The explosions resulted in the pipeline being shut down while technicians of the Colombian Petroleum Enterprise (Ecopetrol) replaced the damaged pipes.Revolutionary Armed Forces of Colombia (FARC)UtilitiesExplosivesUnknown11
1782015712LibyaSirteMiddle East & North AfricaSirte31.19758616.586681Bombing/Explosion0.00.0House of Member07/12/2015: Assailants destroyed a politician's house in Sirte city, Sirte district, Libya. There were no reported casualties in the blast. This was one of 12 similar attacks targeting politicians' houses in Sirte. The Tripoli Province of the Islamic State claimed responsibility for the incidents.Tripoli Province of the Islamic StatePrivate Citizens & PropertyExplosivesThe specific motive is unknown; however, sources posited that the victim's house was targeted because the victim did not support the Tripoli Province of the Islamic State.11
1582014711IraqNinevehMiddle East & North AfricaHammam al-Alil36.15855843.255100Bombing/Explosion0.00.0House of Officer07/11/2014: An explosive device detonated at the residence of a police officer in Hammam al-Alil area, Nineveh governorate, Iraq. This was one of 10 similar attacks on the residences of police officers in this area on July 11, 2014. There were no reported casualties; however, the house was damaged in the blast. No group claimed responsibility for the incident; however, sources attributed the attack to the Islamic State of Iraq and the Levant (ISIL).Islamic State of Iraq and the Levant (ISIL)PoliceExplosivesThe specific motive is unknown; however, sources posited that the Islamic State of Iraq and the Levant (ISIL) carried out the attack in order to punish the police officer for refusing to pledge allegiance to ISIL.10